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<li class="navelem"><b>NeuZephyr</b></li><li class="navelem"><a class="el" href="namespace_neu_zephyr_1_1_nodes.html">Nodes</a></li><li class="navelem"><a class="el" href="namespace_neu_zephyr_1_1_nodes_1_1_loss.html">Loss</a></li>  </ul>
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  <div class="headertitle"><div class="title">NeuZephyr::Nodes::Loss Namespace Reference</div></div>
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<p>Contains loss function nodes for computing various loss metrics in a machine learning model.  
<a href="#details">More...</a></p>
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Classes</h2></td></tr>
<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="class_neu_zephyr_1_1_nodes_1_1_loss_1_1_binary_cross_entropy_node.html">BinaryCrossEntropyNode</a></td></tr>
<tr class="memdesc:"><td class="mdescLeft">&#160;</td><td class="mdescRight">Represents the Binary Cross-Entropy (BCE) loss function node in a computational graph.  <a href="class_neu_zephyr_1_1_nodes_1_1_loss_1_1_binary_cross_entropy_node.html#details">More...</a><br /></td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="class_neu_zephyr_1_1_nodes_1_1_loss_1_1_mean_squared_error_node.html">MeanSquaredErrorNode</a></td></tr>
<tr class="memdesc:"><td class="mdescLeft">&#160;</td><td class="mdescRight">Represents the Mean Squared Error (MSE) loss function node in a computational graph.  <a href="class_neu_zephyr_1_1_nodes_1_1_loss_1_1_mean_squared_error_node.html#details">More...</a><br /></td></tr>
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<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
<div class="textblock"><p>Contains loss function nodes for computing various loss metrics in a machine learning model. </p>
<p>The <code><a class="el" href="namespace_neu_zephyr_1_1_nodes_1_1_loss.html" title="Contains loss function nodes for computing various loss metrics in a machine learning model.">Loss</a></code> namespace provides a collection of nodes that represent different loss functions used during the training of machine learning models. These loss functions are used to evaluate the model's performance by calculating the difference between the predicted output and the true values.</p>
<p>This namespace includes:</p><ul>
<li><b>Regression <a class="el" href="namespace_neu_zephyr_1_1_nodes_1_1_loss.html" title="Contains loss function nodes for computing various loss metrics in a machine learning model.">Loss</a> Functions</b>: <a class="el" href="namespace_neu_zephyr_1_1_nodes.html" title="Contains classes and functionality for nodes in a neural network or computational graph.">Nodes</a> like <code><a class="el" href="class_neu_zephyr_1_1_nodes_1_1_loss_1_1_mean_squared_error_node.html" title="Represents the Mean Squared Error (MSE) loss function node in a computational graph.">MeanSquaredErrorNode</a></code> that compute loss for regression tasks.</li>
<li><b>Classification <a class="el" href="namespace_neu_zephyr_1_1_nodes_1_1_loss.html" title="Contains loss function nodes for computing various loss metrics in a machine learning model.">Loss</a> Functions</b>: <a class="el" href="namespace_neu_zephyr_1_1_nodes.html" title="Contains classes and functionality for nodes in a neural network or computational graph.">Nodes</a> such as <code><a class="el" href="class_neu_zephyr_1_1_nodes_1_1_loss_1_1_binary_cross_entropy_node.html" title="Represents the Binary Cross-Entropy (BCE) loss function node in a computational graph.">BinaryCrossEntropyNode</a></code> for computing loss in binary classification problems.</li>
</ul>
<p><a class="el" href="namespace_neu_zephyr_1_1_nodes_1_1_loss.html" title="Contains loss function nodes for computing various loss metrics in a machine learning model.">Loss</a> function nodes perform key roles in the optimization process by guiding the model to minimize errors during training. These nodes integrate with the model’s computational graph, where they compute the forward pass loss and its gradients during the backward pass.</p>
<dl class="section note"><dt>Note</dt><dd><ul>
<li>The nodes in this namespace are specifically designed to handle loss calculations in supervised learning tasks.</li>
<li>These loss functions are typically combined with optimization algorithms like Gradient Descent during model training.</li>
<li>Ensure that the input tensors are compatible in terms of shape for proper loss computation.</li>
</ul>
</dd></dl>
<dl class="section author"><dt>Author</dt><dd>Mgepahmge (<a href="https://github.com/Mgepahmge">https://github.com/Mgepahmge</a>)</dd></dl>
<dl class="section date"><dt>Date</dt><dd>2024/12/07 </dd></dl>
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